
"""simple docstring"""






import collections
from typing import List, Optional, Union

from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert_fast import BertTokenizerFast
from .tokenization_dpr import DPRContextEncoderTokenizer, DPRQuestionEncoderTokenizer, DPRReaderTokenizer


UpperCAmelCase        =				logging.get_logger(__name__)

UpperCAmelCase        =				{"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""}

UpperCAmelCase        =				{
    """vocab_file""": {
        """facebook/dpr-ctx_encoder-single-nq-base""": (
            """https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/vocab.txt"""
        ),
        """facebook/dpr-ctx_encoder-multiset-base""": (
            """https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/vocab.txt"""
        ),
    },
    """tokenizer_file""": {
        """facebook/dpr-ctx_encoder-single-nq-base""": (
            """https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/tokenizer.json"""
        ),
        """facebook/dpr-ctx_encoder-multiset-base""": (
            """https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/tokenizer.json"""
        ),
    },
}
UpperCAmelCase        =				{
    """vocab_file""": {
        """facebook/dpr-question_encoder-single-nq-base""": (
            """https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/vocab.txt"""
        ),
        """facebook/dpr-question_encoder-multiset-base""": (
            """https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/vocab.txt"""
        ),
    },
    """tokenizer_file""": {
        """facebook/dpr-question_encoder-single-nq-base""": (
            """https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/tokenizer.json"""
        ),
        """facebook/dpr-question_encoder-multiset-base""": (
            """https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/tokenizer.json"""
        ),
    },
}
UpperCAmelCase        =				{
    """vocab_file""": {
        """facebook/dpr-reader-single-nq-base""": (
            """https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/vocab.txt"""
        ),
        """facebook/dpr-reader-multiset-base""": (
            """https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/vocab.txt"""
        ),
    },
    """tokenizer_file""": {
        """facebook/dpr-reader-single-nq-base""": (
            """https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/tokenizer.json"""
        ),
        """facebook/dpr-reader-multiset-base""": (
            """https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/tokenizer.json"""
        ),
    },
}

UpperCAmelCase        =				{
    """facebook/dpr-ctx_encoder-single-nq-base""": 5_1_2,
    """facebook/dpr-ctx_encoder-multiset-base""": 5_1_2,
}
UpperCAmelCase        =				{
    """facebook/dpr-question_encoder-single-nq-base""": 5_1_2,
    """facebook/dpr-question_encoder-multiset-base""": 5_1_2,
}
UpperCAmelCase        =				{
    """facebook/dpr-reader-single-nq-base""": 5_1_2,
    """facebook/dpr-reader-multiset-base""": 5_1_2,
}


UpperCAmelCase        =				{
    """facebook/dpr-ctx_encoder-single-nq-base""": {"""do_lower_case""": True},
    """facebook/dpr-ctx_encoder-multiset-base""": {"""do_lower_case""": True},
}
UpperCAmelCase        =				{
    """facebook/dpr-question_encoder-single-nq-base""": {"""do_lower_case""": True},
    """facebook/dpr-question_encoder-multiset-base""": {"""do_lower_case""": True},
}
UpperCAmelCase        =				{
    """facebook/dpr-reader-single-nq-base""": {"""do_lower_case""": True},
    """facebook/dpr-reader-multiset-base""": {"""do_lower_case""": True},
}



class 			lowercase					( lowercase__		):
			lowercase        =     VOCAB_FILES_NAMES
			lowercase        =     CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
			lowercase        =     CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			lowercase        =     CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION
			lowercase        =     DPRContextEncoderTokenizer





class 			lowercase					( lowercase__		):
			lowercase        =     VOCAB_FILES_NAMES
			lowercase        =     QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
			lowercase        =     QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			lowercase        =     QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
			lowercase        =     DPRQuestionEncoderTokenizer





UpperCAmelCase        =				collections.namedtuple(
    """DPRSpanPrediction""", ["""span_score""", """relevance_score""", """doc_id""", """start_index""", """end_index""", """text"""]
)

UpperCAmelCase        =				collections.namedtuple("""DPRReaderOutput""", ["""start_logits""", """end_logits""", """relevance_logits"""])


UpperCAmelCase        =				r"""
    Return a dictionary with the token ids of the input strings and other information to give to `.decode_best_spans`.
    It converts the strings of a question and different passages (title and text) in a sequence of IDs (integers),
    using the tokenizer and vocabulary. The resulting `input_ids` is a matrix of size `(n_passages, sequence_length)`
    with the format:

    [CLS] <question token ids> [SEP] <titles ids> [SEP] <texts ids>

    Args:
        questions (`str` or `List[str]`):
            The questions to be encoded. You can specify one question for many passages. In this case, the question
            will be duplicated like `[questions] * n_passages`. Otherwise you have to specify as many questions as in
            `titles` or `texts`.
        titles (`str` or `List[str]`):
            The passages titles to be encoded. This can be a string or a list of strings if there are several passages.
        texts (`str` or `List[str]`):
            The passages texts to be encoded. This can be a string or a list of strings if there are several passages.
        padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`):
            Activates and controls padding. Accepts the following values:

            - `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single sequence
              if provided).
            - `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum
              acceptable input length for the model if that argument is not provided.
            - `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different
              lengths).
        truncation (`bool`, `str` or [`~tokenization_utils_base.TruncationStrategy`], *optional*, defaults to `False`):
            Activates and controls truncation. Accepts the following values:

            - `True` or `'longest_first'`: Truncate to a maximum length specified with the argument `max_length` or to
              the maximum acceptable input length for the model if that argument is not provided. This will truncate
              token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a batch
              of pairs) is provided.
            - `'only_first'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum
              acceptable input length for the model if that argument is not provided. This will only truncate the first
              sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
            - `'only_second'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum
              acceptable input length for the model if that argument is not provided. This will only truncate the
              second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
            - `False` or `'do_not_truncate'` (default): No truncation (i.e., can output batch with sequence lengths
              greater than the model maximum admissible input size).
        max_length (`int`, *optional*):
                Controls the maximum length to use by one of the truncation/padding parameters.

                If left unset or set to `None`, this will use the predefined model maximum length if a maximum length
                is required by one of the truncation/padding parameters. If the model has no specific maximum input
                length (like XLNet) truncation/padding to a maximum length will be deactivated.
        return_tensors (`str` or [`~utils.TensorType`], *optional*):
                If set, will return tensors instead of list of python integers. Acceptable values are:

                - `'tf'`: Return TensorFlow `tf.constant` objects.
                - `'pt'`: Return PyTorch `torch.Tensor` objects.
                - `'np'`: Return Numpy `np.ndarray` objects.
        return_attention_mask (`bool`, *optional*):
            Whether or not to return the attention mask. If not set, will return the attention mask according to the
            specific tokenizer's default, defined by the `return_outputs` attribute.

            [What are attention masks?](../glossary#attention-mask)

    Return:
        `Dict[str, List[List[int]]]`: A dictionary with the following keys:

        - `input_ids`: List of token ids to be fed to a model.
        - `attention_mask`: List of indices specifying which tokens should be attended to by the model.
    """



@add_start_docstrings(lowercase__		)
class 			lowercase					:






			def __call__(self  :	List[Any]      ,SCREAMING_SNAKE_CASE_  :	List[str]      ,SCREAMING_SNAKE_CASE_  :	Optional[str] = None      ,SCREAMING_SNAKE_CASE_  :	Optional[str] = None      ,SCREAMING_SNAKE_CASE_  :	Union[bool, str] = False      ,SCREAMING_SNAKE_CASE_  :	Union[bool, str] = False      ,SCREAMING_SNAKE_CASE_  :	Optional[int] = None      ,SCREAMING_SNAKE_CASE_  :	Optional[Union[str, TensorType]] = None      ,SCREAMING_SNAKE_CASE_  :	Optional[bool] = None      ,**SCREAMING_SNAKE_CASE_  :	int      ,)       ->	BatchEncoding:





				"""simple docstring"""


				if titles is None and texts is None:
					return super().__call__(
					    SCREAMING_SNAKE_CASE_      ,padding=SCREAMING_SNAKE_CASE_      ,truncation=SCREAMING_SNAKE_CASE_      ,max_length=SCREAMING_SNAKE_CASE_      ,return_tensors=SCREAMING_SNAKE_CASE_      ,return_attention_mask=SCREAMING_SNAKE_CASE_      ,**SCREAMING_SNAKE_CASE_      ,)
				elif titles is None or texts is None:
					lowerCAmelCase												=  titles if texts is None else texts
					return super().__call__(
					    SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_      ,padding=SCREAMING_SNAKE_CASE_      ,truncation=SCREAMING_SNAKE_CASE_      ,max_length=SCREAMING_SNAKE_CASE_      ,return_tensors=SCREAMING_SNAKE_CASE_      ,return_attention_mask=SCREAMING_SNAKE_CASE_      ,**SCREAMING_SNAKE_CASE_      ,)
				lowerCAmelCase												=  titles if not isinstance(SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_			) else [titles]
				lowerCAmelCase												=  texts if not isinstance(SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_			) else [texts]
				lowerCAmelCase												=  len(SCREAMING_SNAKE_CASE_			)
				lowerCAmelCase												=  questions if not isinstance(SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_			) else [questions] * n_passages
				assert len(SCREAMING_SNAKE_CASE_			) == len(
				    SCREAMING_SNAKE_CASE_			), F"""There should be as many titles than texts but got {len(SCREAMING_SNAKE_CASE_			)} titles and {len(SCREAMING_SNAKE_CASE_			)} texts."""
				lowerCAmelCase												=  super().__call__(SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_      ,padding=SCREAMING_SNAKE_CASE_      ,truncation=SCREAMING_SNAKE_CASE_			)['''input_ids''']
				lowerCAmelCase												=  super().__call__(SCREAMING_SNAKE_CASE_      ,add_special_tokens=SCREAMING_SNAKE_CASE_      ,padding=SCREAMING_SNAKE_CASE_      ,truncation=SCREAMING_SNAKE_CASE_			)['''input_ids''']
				lowerCAmelCase												=  {
				    '''input_ids''': [
				        (encoded_question_and_title + encoded_text)[:max_length]
				        if max_length is not None and truncation
				        else encoded_question_and_title + encoded_text
				        for encoded_question_and_title, encoded_text in zip(SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_			)
				    ]
				}
				if return_attention_mask is not False:
					lowerCAmelCase												=  []
					for input_ids in encoded_inputs["input_ids"]:
						attention_mask.append([int(input_id != self.pad_token_id			) for input_id in input_ids]			)
					lowerCAmelCase												=  attention_mask
				return self.pad(SCREAMING_SNAKE_CASE_      ,padding=SCREAMING_SNAKE_CASE_      ,max_length=SCREAMING_SNAKE_CASE_      ,return_tensors=SCREAMING_SNAKE_CASE_			)






			def 					UpperCAmelCase							(self  :	str      ,SCREAMING_SNAKE_CASE_  :	BatchEncoding      ,SCREAMING_SNAKE_CASE_  :	DPRReaderOutput      ,SCREAMING_SNAKE_CASE_  :	int = 16      ,SCREAMING_SNAKE_CASE_  :	int = 64      ,SCREAMING_SNAKE_CASE_  :	int = 4      ,)       ->	List[DPRSpanPrediction]:





				"""simple docstring"""


				lowerCAmelCase												=  reader_input['''input_ids''']
				lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase												=  reader_output[:3]
				lowerCAmelCase												=  len(SCREAMING_SNAKE_CASE_			)
				lowerCAmelCase												=  sorted(range(SCREAMING_SNAKE_CASE_			)      ,reverse=SCREAMING_SNAKE_CASE_      ,key=relevance_logits.__getitem__			)
				lowerCAmelCase												=  []
				for doc_id in sorted_docs:
					lowerCAmelCase												=  list(input_ids[doc_id]			)
					# assuming question & title information is at the beginning of the sequence
					lowerCAmelCase												=  sequence_ids.index(self.sep_token_id      ,2			) + 1  # second sep id
					if sequence_ids[-1] == self.pad_token_id:
						lowerCAmelCase												=  sequence_ids.index(self.pad_token_id			)
					else:
						lowerCAmelCase												=  len(SCREAMING_SNAKE_CASE_			)

					lowerCAmelCase												=  self._get_best_spans(
					    start_logits=start_logits[doc_id][passage_offset:sequence_len]      ,end_logits=end_logits[doc_id][passage_offset:sequence_len]      ,max_answer_length=SCREAMING_SNAKE_CASE_      ,top_spans=SCREAMING_SNAKE_CASE_      ,)
					for start_index, end_index in best_spans:
						start_index += passage_offset
						end_index += passage_offset
						nbest_spans_predictions.append(
						    DPRSpanPrediction(
						        span_score=start_logits[doc_id][start_index] + end_logits[doc_id][end_index]      ,relevance_score=relevance_logits[doc_id]      ,doc_id=SCREAMING_SNAKE_CASE_      ,start_index=SCREAMING_SNAKE_CASE_      ,end_index=SCREAMING_SNAKE_CASE_      ,text=self.decode(sequence_ids[start_index : end_index + 1]			)      ,)			)
					if len(SCREAMING_SNAKE_CASE_			) >= num_spans:
						break
				return nbest_spans_predictions[:num_spans]






			def 					UpperCAmelCase							(self  :	List[str]      ,SCREAMING_SNAKE_CASE_  :	List[int]      ,SCREAMING_SNAKE_CASE_  :	List[int]      ,SCREAMING_SNAKE_CASE_  :	int      ,SCREAMING_SNAKE_CASE_  :	int      ,)       ->	List[DPRSpanPrediction]:





				"""simple docstring"""


				lowerCAmelCase												=  []
				for start_index, start_score in enumerate(SCREAMING_SNAKE_CASE_			):
					for answer_length, end_score in enumerate(end_logits[start_index : start_index + max_answer_length]			):
						scores.append(((start_index, start_index + answer_length), start_score + end_score)			)
				lowerCAmelCase												=  sorted(SCREAMING_SNAKE_CASE_      ,key=lambda SCREAMING_SNAKE_CASE_			: x[1]      ,reverse=SCREAMING_SNAKE_CASE_			)
				lowerCAmelCase												=  []
				for (start_index, end_index), score in scores:
					assert start_index <= end_index, F"""Wrong span indices: [{start_index}:{end_index}]"""
					lowerCAmelCase												=  end_index - start_index + 1
					assert length <= max_answer_length, F"""Span is too long: {length} > {max_answer_length}"""
					if any(
					    start_index <= prev_start_index <= prev_end_index <= end_index
					    or prev_start_index <= start_index <= end_index <= prev_end_index
					    for (prev_start_index, prev_end_index) in chosen_span_intervals			):
						continue
					chosen_span_intervals.append((start_index, end_index)			)

					if len(SCREAMING_SNAKE_CASE_			) == top_spans:
						break
				return chosen_span_intervals





@add_end_docstrings(lowercase__		)
class 			lowercase					( lowercase__   ,lowercase__		):
			lowercase        =     VOCAB_FILES_NAMES
			lowercase        =     READER_PRETRAINED_VOCAB_FILES_MAP
			lowercase        =     READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			lowercase        =     READER_PRETRAINED_INIT_CONFIGURATION
			lowercase        =     ['''input_ids''', '''attention_mask''']
			lowercase        =     DPRReaderTokenizer




"""simple docstring"""






import warnings

from ...utils import logging
from .image_processing_mobilevit import MobileViTImageProcessor


UpperCAmelCase        =				logging.get_logger(__name__)



class 			lowercase					( lowercase__		):






			def __init__(self  :	Dict      ,*SCREAMING_SNAKE_CASE_  :	str      ,**SCREAMING_SNAKE_CASE_  :	Optional[Any]			)       ->	None:





				"""simple docstring"""


				warnings.warn(
				    '''The class MobileViTFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
				    ''' Please use MobileViTImageProcessor instead.'''      ,SCREAMING_SNAKE_CASE_      ,)
				super().__init__(*SCREAMING_SNAKE_CASE_      ,**SCREAMING_SNAKE_CASE_			)


